Cargando…
Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data
Computer-aided diagnosis (CAD) methods such as the X-rays-based method is one of the cheapest and safe alternative options to diagnose the disease compared to other alternatives such as Computed Tomography (CT) scan, and so on. However, according to our experiments on X-ray public datasets and real...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245274/ https://www.ncbi.nlm.nih.gov/pubmed/37292095 http://dx.doi.org/10.3389/fbioe.2023.1058888 |
_version_ | 1785054830668021760 |
---|---|
author | Yao, Dan Xu, Zhenghua Lin, Yi Zhan, Yuefu |
author_facet | Yao, Dan Xu, Zhenghua Lin, Yi Zhan, Yuefu |
author_sort | Yao, Dan |
collection | PubMed |
description | Computer-aided diagnosis (CAD) methods such as the X-rays-based method is one of the cheapest and safe alternative options to diagnose the disease compared to other alternatives such as Computed Tomography (CT) scan, and so on. However, according to our experiments on X-ray public datasets and real clinical datasets, we found that there are two challenges in the current classification of pneumonia: existing public datasets have been preprocessed too well, making the accuracy of the results relatively high; existing models have weak ability to extract features from the clinical pneumonia X-ray dataset. To solve the dataset problems, we collected a new dataset of pediatric pneumonia with labels obtained through a comprehensive pathogen-radiology-clinical diagnostic screening. Then, to accurately capture the important features in imbalanced data, based on the new dataset, we proposed for the first time a two-stage training multimodal pneumonia classification method combining X-ray images and blood testing data, which improves the image feature extraction ability through a global-local attention module and mitigate the influence of class imbalance data on the results through the two-stage training strategy. In experiments, the performance of our proposed model is the best on new clinical data and outperforms the diagnostic accuracy of four experienced radiologists. Through further research on the performance of various blood testing indicators in the model, we analyzed the conclusions that are helpful for radiologists to diagnose. |
format | Online Article Text |
id | pubmed-10245274 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102452742023-06-08 Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data Yao, Dan Xu, Zhenghua Lin, Yi Zhan, Yuefu Front Bioeng Biotechnol Bioengineering and Biotechnology Computer-aided diagnosis (CAD) methods such as the X-rays-based method is one of the cheapest and safe alternative options to diagnose the disease compared to other alternatives such as Computed Tomography (CT) scan, and so on. However, according to our experiments on X-ray public datasets and real clinical datasets, we found that there are two challenges in the current classification of pneumonia: existing public datasets have been preprocessed too well, making the accuracy of the results relatively high; existing models have weak ability to extract features from the clinical pneumonia X-ray dataset. To solve the dataset problems, we collected a new dataset of pediatric pneumonia with labels obtained through a comprehensive pathogen-radiology-clinical diagnostic screening. Then, to accurately capture the important features in imbalanced data, based on the new dataset, we proposed for the first time a two-stage training multimodal pneumonia classification method combining X-ray images and blood testing data, which improves the image feature extraction ability through a global-local attention module and mitigate the influence of class imbalance data on the results through the two-stage training strategy. In experiments, the performance of our proposed model is the best on new clinical data and outperforms the diagnostic accuracy of four experienced radiologists. Through further research on the performance of various blood testing indicators in the model, we analyzed the conclusions that are helpful for radiologists to diagnose. Frontiers Media S.A. 2023-05-17 /pmc/articles/PMC10245274/ /pubmed/37292095 http://dx.doi.org/10.3389/fbioe.2023.1058888 Text en Copyright © 2023 Yao, Xu, Lin and Zhan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Yao, Dan Xu, Zhenghua Lin, Yi Zhan, Yuefu Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data |
title | Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data |
title_full | Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data |
title_fullStr | Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data |
title_full_unstemmed | Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data |
title_short | Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data |
title_sort | accurate and intelligent diagnosis of pediatric pneumonia using x-ray images and blood testing data |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245274/ https://www.ncbi.nlm.nih.gov/pubmed/37292095 http://dx.doi.org/10.3389/fbioe.2023.1058888 |
work_keys_str_mv | AT yaodan accurateandintelligentdiagnosisofpediatricpneumoniausingxrayimagesandbloodtestingdata AT xuzhenghua accurateandintelligentdiagnosisofpediatricpneumoniausingxrayimagesandbloodtestingdata AT linyi accurateandintelligentdiagnosisofpediatricpneumoniausingxrayimagesandbloodtestingdata AT zhanyuefu accurateandintelligentdiagnosisofpediatricpneumoniausingxrayimagesandbloodtestingdata |